Method and detector for a novel channel quality indicator for space-time encoded MIMO spread spectrum systems in frequency selective channels

ABSTRACT

In a JE, spread spectrum communication MIMO system where a demultiplexed packet is transmitted over multiple streams, two versions of a single CQI are disclosed: generalized SNR or constrained mutual information CMI between the transmitted and received chip vectors. In the receiver, the CMI is constrained so that filtering is suboptimal. The filter bank is preferably LMMSE or MVDR filters that convert the multi-path channel into a single path channel so that joint sequence detection is unnecessary. Detection is by a per-Walsh code architecture, wherein a plurality of Walsh-code specific detectors in parallel detect bits or symbols from the single-channel chips after downconverting chip to symbols. Link to system mapping is realizable using the disclosed CQI for a JE MIMO system in that the CQI or related information is returned to the transmitter, which adapts coding rate and/or modulation for the channel as represented by the CQI.

FIELD OF THE INVENTION

The present invention relates to space-time encoded spread spectrumcommunication systems such as CDMA using at least two transmit and/ortwo receive antennas. It is particularly related to a feedback mechanismby which a receiver filter may be optimized for such a system.

BACKGROUND

Multiple transmit, multiple receive antenna (multiple input/multipleoutput or MIMO) systems offer potential for realizing high spectralefficiency of a wireless communications system. Information theoreticstudies establish that in an independent flat-fading channelenvironment, the capacity of such an MIMO system increases linearly withthe number of antennas. One such practical MIMO configuration is BellLabs' Layered Space-Time (BLAST) system, which realizes high spectralefficiency for a narrow-band TDMA system. MIMO schemes are also beingconsidered for standardization in WCDMA/HSDPA, and may be considered forCDMA2000 as well in the near future, both for the downlink of the codedivision multiple access (CDMA) systems.

Diagonal BLAST presumes that the MIMO channel is Rayleigh fading andthat the channel parameters are known at the receiver but not at thetransmitter, and is therefore an open-loop approach. V-BLAST, which is asimpler implementation of diagonal BLAST, advocates a simpledemultiplexing of the single data streams instead of some specificencoding in space-time. The corresponding receiver architecture forV-BLAST is also simpler. In general, the various BLAST approachestransmit at the same rate on each transmit antenna or antenna pair(depending upon feedback and spatial channel realization), and use aminimum mean square error linear transformation at the receiver followedby interference cancellation based on coded symbols. Because of its openloop approach, V-BLAST uses a simple demultiplexing of the symbols ofthe encoded packet over multiple antennas.

One key aspect of MIMO system research is to design receivers that canreliably decode the transmitted signals in a frequency-selectivechannel. For a single input, single output (SISO) CDMA link, chip-levelequalization is a promising means of improving the receiver performancein a frequency selective channel. Two major types of FIR linearequalization exist, namely the non-adaptive linear equalization that isbased on either linear minimum mean square error (LMMSE) or minimumvariance distortionless response (MVDR), and the adaptive linearequalization. Another alternative is the recursive Kalman filteringapproach, where it is shown to outperform the LMMSE approach at aslightly higher complexity. Applying MIMO configuration to the CDMAdownlink presents additional challenge to the receiver design, as thereceiver has to combat both the interchip interference (ICI) and theco-channel interference (CCI) in order to achieve reliablecommunication. It has been shown that both LMMSE algorithm and theKalman filter algorithm can be extended to the MIMO system.

Apart from improving the performance of MIMO transmission through betterreceiver design, the study of such advanced receivers leads to a betterunderstanding of the characterization of the MIMO link. Suchcharacterization is very important from the overall system evaluationperspective. Specifically, the air interface in a cellular systemconsists of links between the base stations (BS) and the terminals, alsoknown as the mobile stations (MS). The performance of the air interfaceis quantified by simulating these links individually. It is practicallyimpossible to embed a bit-true simulation of each of these links into asystem level simulation. Fortunately, only a limited amount ofinformation is required by the upper layers from the physical layer,such as frame and packet errors, signaling errors etc. Thus, analternative to exhaustive link simulation is widely used, wherein theseparameters are modeled in a random manner while still confirming totheir statistical behavior as predicted by individual link simulations.This process of abstraction of the link performance is known aslink-to-system mapping. One of the functions of this mapping is to usesome measure of the link quality, like signal to noise ration (SNR), toestimate the frame error rate (FER) that can be expected.

Such link-to-system mapping procedures have been studied and used in thepast, predominantly for SISO links. To facilitate an explanation oflink-to-system mapping for MIMO schemes, it is stipulated that from thepoint of view of packet transmission with forward error correctioncoding, MIMO transmission can be classified into two broad categories:jointly encoded (henceforth denoted as JE) and separately encoded (SE).In the JE mode of transmission, as the name suggests, a single encodedpacket is transmitted over multiple streams after de-multiplexing,whereas in SE, each stream consists of a separately encoded packet.Coded-VBLAST and its variants, as well as trellis coded space-timemodulation schemes, fall under the first category, while Per AntennaRate Control (PARC) and its variants belong to the second category. Theapproach to the SNR vs. FER mapping issue depends upon the type oftransmission scheme being utilized. Even under quasi-static channelconditions, the SE schemes are such that each stream, afterequalization, sees a single SNR associated with itself, and hence, themapping to FER is a two-dimensional problem, just as in the SISO case.

The problem has been resolved for SISO systems in 3rd GenerationPartnership Project 2 (3GPP2), “1x EV-DV Evaluation Methodology,” 2001.Solutions for a MIMO system with separate encoding have also beenproposed in at least three different papers: “Approaching eigenmodeBLAST channel capacity using VBLAST with rate and power feedback,” inProceedings of IEEE Vehicular Technology Fall Conference, pp. 915-919,October 2001 by S. T. Chung, A. Lozano, and H. Huang; “Contribution to3GPP: R1-010879: Increasing MIMO Throughput with Per-Antenna RateControl,” 2001, by Lucent; and “Contribution to 3GPP: R1-040290: DoubleSpace Time Transmit Diversity with Sub-Group Rate Control (DSTTD-SGRC)for 2 or More Receive Antennas,” 2004, by Mitsubishi.

These solutions are not readily adaptable for use in a JE MIMO systembecause in JE schemes, various portions of a packet see different SNRs,and hence the mapping is potentially a multi-dimensional problem. Theinventors are unaware of any proposal in the prior art for a CQI in ajoint space-time encoded (JE) MIMO scheme in a frequency-selectivechannel. What is needed in the art is a channel quality indicator (CQI)that accurately characterizes the wireless link in a MIMO system thatuses joint encoding. Such a CQI is essential for both link adaptationand link to system mapping in system level evaluations. A receiver thatuses such a CQI would aid in realizing the theoretic capacity increasesoffered by JE MIMO communication systems.

SUMMARY OF THE INVENTION

This invention is in one aspect a method for detecting a jointly encodedsignal received over a multi-path channel. The method includes receivinga jointly encoded signal over a multi-path channel by N receiveantennas, wherein N is an integer greater than one. For each of the Nreceive antennas, the received signal is sampled within a chip intervalto resolve an antenna-wise chip vector for each of the N receiveantennas. These antenna-wise chip vectors are filtered as a block usinga channel quality indicator CQI. The CQI describes a multi-path channelover which the jointly encoded signal was received. Further in themethod, the filtered block is downconverted to one of bits and symbols.An important aspect in the method is that, for each spreading code bywhich the jointly encoded signal is spread, the downconverted bits orsymbols are detected in parallel. As detailed below, the CQI ispreferably constrained mutual information between an estimatedtransmitted chip vector and the received block of chip-wise signalvectors.

In another aspect, the invention is a method for detecting symbols of ajointly encoded, spread spectrum signal. In this method, a signal isreceived from a multi-path channel over at least two receive antennas ina chip interval, and sampled within the chip interval to achieve achip-wise signal vector from each receive antenna. These chip-wisesignal vectors are stored as a block, and the multi-path channel isestimated using the block of chip-wise signal vectors. Using thatestimate of the multi-path channel, the block of chip-wise signalvectors is filtered to restore othogonality to spreading codes that wereused to spread the signal in transmission. The filtered block ofchip-wise signal vectors is down-converted, descrambled and despread toyield parallel outputs of symbol-level signal vectors, each paralleloutput corresponding to a spreading code. For each of the paralleloutputs, one of bits or symbols are spatially detected using onespreading code.

In yet another aspect, the present invention is a method for adaptingtransmissions in a wireless communication system. The method is dividedamong a first and second transceiver. In the first transceiver, a firstsignal to be transmitted is jointly encoded at a first coding rate, andmodulated with a first modulation, such as QPSK or 16-QAM for example.The jointly encoded and modulated first signal is transmitted over aspread spectrum multi-path wireless channel by at least one transmitantenna. In the second transceiver, the jointly encoded and modulatedfirst signal is received by at least two receive antennas over themulti-path channel, the multi-path channel is converted to an effectivesingle-path channel, and a single channel quality indicator CQI isdetermined that characterizes the effective single-path channel. Stillin the second transceiver and from the effective single-path channel,one of bits and symbols are detected in parallel, each paralleldetection being according to one spreading code by which the firstsignal is spread over the spectrum. The second transceiver alsotransmits to the first transceiver a feedback based on the CQI, which ispreferably the CQI itself or an estimated frame error rate derived fromthe CQI. Further in the method and in the first transceiver, thefeedback is received, a second signal to be transmitted is jointlyencoded and modulated, and the jointly encoded and modulated secondsignal is transmitted over the spread spectrum multi-path wirelesschannel by the at least one transmit antenna. An aspect of this methodis that, in response to the feedback, at least one of the coding rateand the modulation of the second signal differs from that of the firstsignal.

Another aspect of the present invention is a receiver that has at leasttwo receive antennas, a filter bank of linear filters having a firstinput coupled to an output of each receive antenna and a second input, achannel estimator, and a plurality of joint detectors in parallel withone another. The filter bank is for equalizing signal vectors receivedover sub-channels of a multipath channel into signal vectors of a singlechannel. The channel estimator has an input coupled to an output of eachreceive antenna and an output coupled to the second input of the filterbank. The joint detectors each have an input coupled to an output of thefilter bank and an output coupled to a decoder, and each joint detectoris for detecting one of bits or symbols according to one spreading code.The receiver also has a chip-to-symbol down-converter, a de-scrambler,and a de-spreader, each disposed between the filter bank and theplurality of joint detectors.

In yet another aspect, the present invention is a transmitter that hasan encoder, a modulator, a spreader, a multiple number M of transmitantennas, and a processor. The encoder is for jointly encoding an inputsignal into a set of at least one symbol that spans a chip. Themodulator is for modulating the set of at least one symbol onto acarrier wave. The spreader has an input coupled to an output of theencoder and an output of the modulator for spreading the set of at leastone symbol according to a series of spreading codes. Preferably, theencoder and modulator are combined into a signal-space encoder thatperforms both encoding and modulation together, in which case thespreader has an input coupled to an output of the signal space encoder.The processor has an input coupled to a wireless feedback channel and anoutput coupled to at least one of the encoder and the modulator. Inresponse to a channel quality feedback, the processor causes at leastone of the encoder to change an encoding rate and the modulator tochange a modulation.

These and other features, aspects, and advantages of embodiments of thepresent invention will become apparent with reference to the followingdescription in conjunction with the accompanying drawings. It is to beunderstood, however, that the drawings are designed solely for thepurposes of illustration and not as a definition of the limits of theinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a MIMO communication system having atransmitter with M antennas and a receiver with N antennas, and is aprior art context for the present invention.

FIG. 2 is a prior art block diagram showing the transmitted signal thatis detected and decoded according to the present invention.

FIG. 3A is a block diagram of a receiver according to the presentinvention.

FIG. 3B is a block diagram of a transmitter according to the presentinvention.

FIG. 4 is a graph of bit throughout versus geometry comparing V-BLAST toPARC MIMO systems.

FIG. 5 is a graph showing frame error rate versus generalized SNR.

FIG. 6 is a graph showing frame error rate versus constrained mutualinformation.

DETAILED DESCRIPTION

The present invention relates to a novel channel quality indicator (CQI)for space-time jointly encoded MIMO CDMA systems in frequency selectivechannels. In general, the inventive CQI is based on a so-calledper-Walsh code joint detection structure consisting of a front-endlinear filter followed by joint symbol detection among all the streams.The existence of a multipath channel destroys orthogonality amongWalsh-type spreading codes when joint encoding is used at thetransmitter, evident in the fact that in a RAKE receiver, a noise flooris reached at a frame error rate above 0.1. The linear filters describedherein are designed to transform the multipath channel to a single-pathchannel to restore orthogonality of Walsh codes and to avoid the needfor joint sequence detection. These filters maximize the so-calledconstrained mutual information, and LMMSE and MVDR equalizers belong tothis class of filters. Similar to the notion of generalized SNR (GSNR),constrained mutual information provides a CQI measure that describes theMIMO link quality.

Being based on a measure of channel quality, the communication system inwhich the present invention applies and is most advantageous isrelevant. FIG. 1 is a prior art block diagram of a MIMO communicationsystem 20 that serves as context for the present CQI and the followingdiscussion. The communication system 20 includes a transmitter 22 thattransmits over a plurality M of transmit antennas 24 to a receiver 26having a plurality N of receive antennas 28. Transmissions occur over amultipath channel 30, wherein each path or sub-channel is denoted ash_(n,m), where the lowercase subscripts n and m refer to the n^(th)receive antenna 24 and the m^(th) transmit antenna 26. For ease ofdiscussion, assume the transmitter is within a cellular base station andthe receiver is within a mobile station such as a cellular mobiletelephone. In practice, each of the base station and mobile stationemploy both transmitter and receiver at different time instants.

At the transmitter 22, a series of information bits 32 is input into acoding and modulation block 34 that parses the information bits intopackets after encoding, making the system a joint-encoded MIMO system20. The coding and modulation block 34 also includes a serial toparallel converter that outputs M versions of the packets to M spreadingand scrambling blocks 36. It is important to note that the modulatedpackets or symbol streams are demultiplexed prior to transmission,preferably within the channel coding and modulation block 34. Channelcoding may be either jointly over transmit antennas 24, or separatelyfor different transmit antennas 24.

The spreading and scrambling blocks 36 each use a spreading code k suchas a Walsh code to spread the packets among various windows defined bytime and frequency. Each of the spreading and scrambling blocks 36output to one of the transmit antennas 24, which each transmits thepackets or symbol streams over a plurality of sub-channels. For example,the first m=1 transmit antenna 24 transmits each packet or symbol streamover sub-channels h_(1,1), h_(2,1), h_(3,1), . . . h_(N,1). The sameholds true for each of the remaining transmit antennas. As such, thesame packet is subjected to different SNR due to the varioussub-channels over which it is transmitted.

At the receiver 26, each of the N receive antenna 28 receives over eachof the sub-channels. For example, the first n=1 receive antenna 28receives from each of the M transmit antennas 24 over the sub-channelsh_(1,1), h_(1,2), h_(1,3), . . . h_(1,M). The remaining receive antennas28 receive similarly. The output of the receive antennas 28 is collectedin a detection and decoding block 38.

Denote the number of active users in the system as U and the number ofWalsh codes 42 assigned to these users as K₁, . . . , K_(U), where

$K \equiv {\sum\limits_{u = 1}^{U}K_{u}}$is the total number of active Walsh codes. Without loss of generality,the ensuing description assumes that the first user u=1 is the user ofinterest. FIG. 2 is a block diagram showing the signal model at thetransmitter 22, wherein one spreading and scrambling block 36 of FIG. 1is segmented into a series of K_(u) spreading blocks 40 that each applyone of K_(u) spreading codes 42, and a scrambling block 44 where thespread symbols are scrambled prior to transmission from the m^(th)transmit antenna 24. The signal model at the m^(th) transmit antenna 24is given as follows,

$\begin{matrix}{{d_{m}(i)} = {{c(i)}{\sum\limits_{k = 1}^{K}{\sum\limits_{j}{\alpha_{k}{\alpha_{k,m}(j)}{s_{k}( {i - {jG}} )}}}}}} & (1)\end{matrix}$where G is the spreading gain of the system, i, j, m and k are indicesfor the chip, symbol, transmit antenna 24 and spreading code 42,respectively.

Although practical systems such as 1x EV-DV use different spreadinggains for data and voice traffic, this description assumes a fixedspreading gain for simplicity of notation; adaptations for variablespreading gains follows logically. Note that by definition j=┌i/G┐,where ┌*┐ denotes ceiling operation. The base station scrambling code isdenoted by c(i); and the power assigned to spreading code k is denotedby α_(k) (assuming for simplicity that for a given Walsh code k, theamplitudes are the same for all transmit antennas 24, extension to MIMOsystems with uneven powers across transmit antennas 24 followslogically). The term α_(k,m)(j) represents the j^(th) symbol transmittedat the m^(th) transmit antenna 24 on the k^(th) Walsh code, and the terms_(k)=[s_(k)(1), . . . , s_(k)(G)]^(T) is the k^(th) Walsh code 42 _(k).Note that this model implicitly assumes that the same set of Walsh codes42 are used across all the transmit antennas 24.

The transmitted signals propagates through the MIMO multi-path fadingchannel 30 denoted by H ₀, . . . H _(L), where each matrix is ofdimension NΔ×M, where Δ denotes number of samples per chip. The signalmodel at the receive antennas 28 are thus given by the followingequation, after stacking up the received samples across all the receiveantennas for the i^(th) chip interval:

$\begin{matrix}{{\overset{\_}{y}}_{i} = {{\sum\limits_{l = 0}^{L}{{\overset{\_}{H}}_{l}{\overset{\_}{d}}_{i - 1}}} + {\overset{\_}{n}}_{i}}} & (2)\end{matrix}$

Note that y _(i)[_(i,1) ^(T), . . . , y _(i,N]) ^(T) is of length NΔ,and each small vector y _(i,n), includes all the temporal samples withinthe i^(th) chip interval. Meanwhile, L is the channel memory length, d_(i−l)=[d_(i)(i−1), . . . , d_(M)(i−l)]^(T) is the transmitted chipvector at time i−l, and n _(i) is the NΔ×1 dimensional white Gaussiannoise vector with n _(i)≈N( 0 , σ²Ī_(NΔ)). Note that σ² denotes noisevariance and Ī_(NΔ) is the identity matrix of size NΔ×NΔ.

Furthermore, in order to facilitate the discussion on the linear filtersat the receiver, stack up a block of 2F+1 small received vectors (thenotation of 2F+1 suggests that the filters are “centered” with F taps onboth the causal and anti-causal side):y _(i+F:i−F) = Hd _(i+F:i−F−L) + n _(i+F:i−F)  (3)where 2F+1 is the length of the LMMSE equalizing filter and

${{\overset{\_}{y}}_{i + {F:{i - F}}} = \lbrack {{\overset{\_}{y}}_{i + F}^{T},\ldots\mspace{11mu},{\overset{\_}{y}}_{i - F}^{T}} \rbrack^{T}},( {( {{2F} + 1} )N\;\Delta \times 1} )$${{\overset{\_}{n}}_{i + {F:{i - F}}} = \lbrack {{\overset{\_}{n}}_{i + F}^{T},\ldots\mspace{11mu},{\overset{\_}{n}}_{i - F}^{T}} \rbrack^{T}},( {( {{2F} + 1} )N\;\Delta \times 1} )$${{\overset{\_}{d}}_{i + {F:{i - F - L}}} = \lbrack {{\overset{\_}{d}}_{i + F}^{T},\ldots\mspace{11mu},{\overset{\_}{d}}_{i - F - L}^{T}} \rbrack^{T}},( {( {{2F} + L + 1} )M \times 1} )$${\overset{\_}{H} = \begin{bmatrix}{\overset{\_}{H}}_{0} & \ldots & {\overset{\_}{H}}_{L} & \; & \; \\\; & ⋰ & \; & ⋰ & \; \\\; & \; & {\overset{\_}{H}}_{0} & \ldots & {\overset{\_}{H}}_{L}\end{bmatrix}},( {( {{2F} + 1} )N\;\Delta \times ( {{2F} + L + 1} )M} )$where the dimensions of the matrices are given in the parenthetical tothe right of the matrix definition. To keep the notation more intuitive,the subscripts are kept at a “block” level. For instance, y _(i+F:i−F)is the vector that contains blocks y _(i+F), . . . , y _(i−F), whereeach block is a vector of size NΔ×1. The transmitted chip vector d_(i+F:i−F−L) is assumed to be zero mean, white random vectors whosecovariance matrix is given by R _(dd)=σ_(d) ²Ī_(2F+L+1). Furthernotation is defined for future use: d _(ī) = d _(i+F:i−F−L)\ d _(i),where d _(i+F:i−F−L)\ d _(i) denotes the sub-matrix of d _(i+F:i−F−L)\ d_(i) that includes all the elements of d _(i+F:i−F−L) except those in d_(i).

Using this notation, the signal model of equation (3) is rewritten as:y _(i+F:i−F) = H _(i+F:i−F−L) + n _(i+F:i−F) = H ₀ d _(i) + H ₀ d _(ī) +n _(i+F:i−F),  (4)where the submatrix H ₀ = H\ H ₀ is as described above. Furthermore, thecovariance matrix of the received signal y _(i+F:i−F) is defined as R=E[ y _(i+F:i−F) y _(i+F:i−F)=σ_(d) ² HH ^(H)+σ²Ī] and a related matrixR{circumflex over (=)} R−σ_(d) ² H ₀ H ₀ ^(H)=σ_(d) ² H _(0 H) ₀^(H)=σ²Ī.

One prior art approach to detect joint space-time encoded signals is theVector Viterbi Algorithm (VVA), an optimal detector described by W. V.Etten in an article entitled “Maximum-Likelihood Receiver for MultipleChannel Transmission Systems,” IEEE Transactions on Communications, vol.COM-24, pp. 276-284, February 1976. The VVA jointly detects thecollection of symbols α{circumflex over (=)}{α_(k,m)(j)} for all valuesof k, m and j by maximizing the conditional density of the receivedsignal within the block of length N_(b):

$\begin{matrix}{{\overset{\_}{a}}^{opt} = {\underset{\overset{\_}{a}\;}{\arg\mspace{11mu}\max}\mspace{11mu}{f_{\overset{\_}{y}|\overset{\_}{a}}( \overset{\_}{y} \middle| \overset{\_}{a} )}}} & (5)\end{matrix}$where y{circumflex over (=)} y _(0:N) _(b) is the overall signal in theblock, α ^(opt) is the optimal solution of α and the conditional densityfunction of y is denoted by ƒ _(y| α) ( y| α).

To evaluate the complexity of the VVA algorithm in a jointly encodedMIMO system, assume for a moment that the modulation size Q is the sameacross all the transmit antennas 24. Furthermore, it is noted that formost practical systems the channel length is less than the spreadinggain, i.e., L<G, meaning that although the interchannel interference ICImemory length is L, the intersymbol interference ISI memory length isjust L_(ISI)=1. With these assumptions, the complexity of this algorithmmeasured by the number of Euclidean Distance (ED) computations isθ(Q^((L) ^(ISI) ^(+1)MK)ε)=θ(Q^(2MK)ε) where ε denotes the EuclideanDistance computation. Note that although we are only interested in thesymbols carried on the first K₁ Walsh codes of the desired user, thenature of the signal model requires the VVA to be applied jointly on allK Walsh codes. The detection complexity of the VVA is prohibitivelyhigh, even after some complexity reduction using the set-partition basedsub-optimal approach, such as described by N. Benvenuto, R. Sandre, andG. Sostrato in an article entitled “Reduced-State Maximum-LikelihoodMultiuser Detection for Down-Link TD-CDMA Systems,” IEEE Journal onSelected Areas in Communications, vol. 20, pp. 264272, Feb. 2002; andalso by J. Zhang, H. Berg, A. Sayeed, and B. VanVeen in an articleentitled “Reduced-state MIMO sequence estimation for EDGE systems,” inProceedings of Asilomar Conference, 2002. Other drawbacks of applyingVVA in this problem include: a) the unrealistic assumption of knowingall K active Walsh codes, and b) further difficulties in handling themulti-rate signaling in practical CDMA systems. For example, the CDMA 1xEV-DV system allows simultaneous transmission of data traffic withspreading gain of 3.2 and voice traffic with spreading gain of 64 or128.

To circumvent the problems associated with the optimal joint VVAsequence detection method, the present invention focuses on a class ofsub-optimal receivers with the so-called per Walsh code joint detectionstructure, as illustrated in FIG. 3A. FIG. 3A is a block diagram of areceiver 48 according to the present invention. A plurality M of atleast two receive antennas 28 receive the signal over the multi-pathchannel. The received signal from each antenna 28 is sampled Δ times perchip interval as above, and the samples from each receive antenna 28 arestacked and stored as shown generically in equation (2) and morespecifically for centered filter taps in equation (3). Demodulation andsampling blocks are not shown in FIG. 3, but would be disposed betweenthe receive antennas 28 and a depicted front-end filter bank 52.

Signal samples within a single chip interval are assembled into a blockof chip-wise signal vectors 50 that are input into the linear filterbank 52 and to a channel estimator 54 that provides an estimated channel56 back to the filter bank 52. In a preferred embodiment of the receiver48, the linear filter bank 52 W (of size (2F+1)NΔ×M) transforms themulti-path MIMO channel into an effective single-path MIMO channel insome optimal fashion. This is termed equalizing the channels of themulti-path channel, and results in the filter bank output 58:r _(i) = W ^(H) y _(i+F:i−F) = W ^(H) H ₀ d _(i) + W ^(H) H ₀ d _(ī) W^(H) n   (6)where the M×M matrix W ^(H) H ₀ denotes the effective post-filteringsingle-tap MIMO channel, ñ{circumflex over (=)} W ^(H) H ₀ d ₀ + W ^(H)n _(i+F:i−F)≈N( 0 ,δ² W ^(H) R W), is the M×1 post-filteringinterference plus noise. Furthermore, recall that c(i) is the scramblingcode and that j=┌i/G┐ is the symbol index.

The matrix C(j){circumflex over (=)}diag{c(jG), . . . c(jG+G−1)} isdefined as the diagonal matrix that denotes the scrambling operation forthe j^(th) symbol interval. With this nomenclature, a composite block 60performs chip-to-symbol down-conversion, de-scrambling and de-spreadingon the collection of chip vectors { r _(jG), . . . , r _(jG+G−)1}, andthe symbol-level signal vectors 62 _(k) of the composite block 60 may beexpressed as:t _(k)(j)=[ r _(jG) , . . . , r _(jG+G−1) ] C ^(H)(j) s (k)=α_(k) W ^(H)H ₀ α _(k)(j)+{circumflex over (n)}  (7)where k=1, . . . K₁, and α _(k)(j){circumflex over (=)}[α_(k,1)(j), . .. , α_(k,M)(j)]^(T) is the transmitted symbol vector carried on thek^(th) Walsh code for the j^(th) symbol interval and

$\hat{n} \approx {{N( {\overset{\_}{0},{\frac{\sigma^{2}}{G}{\overset{\_}{W}}^{H}\overset{\overset{\_}{\_}}{R}\overset{\_}{W}}} )}.}$Note that equation (7) implicitly uses the facts that: a) the Walshcodes are ortho-normal, i.e., s _(k1) ^(T) s _(k2)=δ_(k1,k2); and b) thescrambling code is pseudo-random, i.e., E[c(i1)c*(i2)]=δ_(i1,i2) whereE[•] denotes expectation operation and (•)* denotes conjugate operation.The outputs 62 of the composite block 60 are in parallel, and each isspecific to one spreading code k for the receiver corresponding to oneuser (e.g., u=1 as above). This is a complexity reduction as compared tothe VVA approach noted above, which uses all K spreading codes.

All that remains is to generate the soft bits for the decoder from thesymbol level signal vectors 62 _(k) t ₁(j), . . . t _(K1)(j). As eachsymbol-level signal vector 62 correlates to only one spreading code forthe u^(th) user, a plurality of K₁ Walsh code joint detectors 64 _(k)detect bits from the symbol vectors 62 that are input to them. These aregenerally output as soft decision bits subject to change in the decoder66. It is noted that if a non-binary channel code is used, the softsymbols should be passed to the decoder in place of soft bits. However,a binary channel code is assumed in this description for ease ofexposition. Let Q_(b){circumflex over (=)}log₂ Q be the number of bitsmapped to each symbol and let b_(k,m) ¹(j), . . . b_(k,m) ^(Q) ^(b) (j)be the bits mapped to the symbol a_(k,m)(j).

The output soft bits are given as the well-known log-likelihood ratios(LLR):

$\begin{matrix}{{{LLR}\lbrack {b_{k,m}^{q}(j)} \rbrack} = {\ln\frac{\{ {\sum\limits_{{{\overset{\_}{a}}_{k}{(j)}} \in A_{q,1}}{f_{b❘\overset{\_}{a}}( {{b_{k,m}^{q}(j)} = {1❘{{\overset{\_}{a}}_{k}(j)}}} )}} \}}{\{ {\sum\limits_{{{\overset{\_}{a}}_{k}{(j)}} \in A_{q,0}}{f_{b❘\overset{\_}{a}}( {{b_{k,m}^{q}(j)} = {0❘{{\overset{\_}{a}}_{k}(j)}}} )}} \}}}} & (8)\end{matrix}$for q=1, . . . , Q_(b); k=1, . . . , K₁; and m=1, . . . , M. Note thatthe set A_(q,1) is defined as A_(q,1){circumflex over (=)}{A11 ā_(k)(j)such that b _(k,m) ^(q)(j)=1}, and A_(q,0) is similarly defined. Theper-Walsh code joint detection approach offers a two-fold complexityreduction benefit compared with the optimal VVA sequence detection.First, the user only need to detect symbols carried on its own Walshcodes (codes 1 to K₁); second, the effective channel in equation (7) ismemoryless and the joint detection occurs only in the spatial dimension.The complexity of the per-Walsh code joint detection is given byθ(K₁Q^(M)ε), which is a dramatic reduction from θ(Q^(2MK)ε) of VVA.

The above description of the structure of the per-Walsh code jointdetection presumes knowledge of the front-end linear filter W. Followingis a description of the front-end linear filter and how to optimize it.The mutual information is used as the measure of optimality in obtainingthe optimal W, and it will be shown that such a solution coincides withthe linear minimum mean square error (LMMSE) or minimum variancedistortionless response (MVDR) solutions. These solutions also provideintuitively pleasing Channel Quality Indicators (CQI) for the link tosystem mapping.

The described filter Wprovides the maximum mutual information betweenthe transmitted and received chip vectors d _(i) and 7, ( W) , where r_(i) is rewritten as r _(i)( W) to signify its dependence on W. If d_(i) is assumed to be Gaussian in order to obtain a closed-formsolution, the problem is really maximizing the (Gaussian) upper bound ofthis mutual information.

Theorem: Assuming d _(i) to be Gaussian, the conditional mutualinformation I( d _(i); r _(i)( W)|H) is maximized by W _(MC)= R ⁻¹ H ₀Āfor any M×M invertible matrix Ā (where the subscript MC representsmaximum capacity).

Proof: Since d _(i) is Gaussian, r _(i)( W) is also Gaussian. Thismutual information is I( d _(i); r _(i)( W)| H)=H( r _(i)( r _(i)(W)|H)−H( r _(i)( W)| H, d _(i))=log det( W ^(H) R W)−log det( Whu H RW). The optimal filter W _(MC) may be obtained by solving

$\begin{matrix}\begin{matrix}{{\overset{\_}{W}}_{MC} = {{\arg\mspace{11mu}{\max\limits_{\;\overset{\_}{W}\;}\;{\log\mspace{11mu}{\det( {{\overset{\_}{W}}^{H}\overset{\_}{R}\overset{\_}{W}} )}}}} - {\log\mspace{11mu}{\det( {{\overset{\_}{W}}^{H}\overset{\overset{\_}{\_}}{R}\overset{\_}{W}} )}}}} \\{= {\arg\mspace{11mu}{\max\limits_{\;\overset{\_}{W}\;}\;{\log\mspace{11mu}{\det( {{\overset{\_}{I}}_{M} + {\sigma_{d}^{2}{\overset{\_}{W}}^{H}{\overset{\_}{H}}_{0}{\overset{\_}{H}}_{0}^{H}{\overset{\_}{W}( {{\overset{\_}{W}}^{H}\overset{\overset{\_}{\_}}{R}\overset{\_}{W}} )}^{- 1}}} )}}}}}\end{matrix} & (9)\end{matrix}$where Ī_(M) is an identity matrix of size M×M. Direct optimization ofequation (9) is difficult, given that W is a (2F+1)NΔ×M matrix. The DataProcessing Lemma outlined in Elements of Information Theory, by T. M.Cover and J. A. Thomas (published by Wiley Interscience, 1991) is usedto provide an upper bound on the mutual information I( d _(i); r _(i)(W)| H), and then to show that the bound is achievable. To this end, notethat since r _(i)( W)= W ^(H) y _(i+F:i−F), d _(i)→ y _(i+F:i−F)→ r_(i)( W) forms a Markov chain, conditioned on the knowledge of thechannel H.

Therefore, by the Data Processing Lemma, the inequalityI( d _(i) ; r _(i)( W )| H )≦I( y _(i) ;y _(i+F;i−F) | H )  (10)holds for any filter W. From the signal model y _(i+F:i−F)= H ₀ d _(i)+H ₀ d _(ī)+ n _(i+F;i−F), one can show that

$\begin{matrix}\begin{matrix}{{I( {{\overset{\_}{d}}_{i}; {\overset{\_}{y}}_{{i + F};{i - F}} \middle| \overset{\_}{H} } )} = {{H( {\overset{\_}{y}}_{{i + F};{i - F}} \middle| \overset{\_}{H} )} - {H( {\overset{\_}{y}}_{{i + F};{i - F}} \middle| \overset{\_}{H} )}}} \\{= {\log\mspace{11mu}{\det( {{\overset{\_}{I}}_{{({{2F} + 1})}N\;\Delta} + {\sigma_{d}^{2}\;{\overset{\overset{\_}{\_}}{R}}^{- 1}{\overset{\_}{H}}_{0}{\overset{\_}{H}}_{0}^{H}}} )}}} \\{= {\log\mspace{11mu}{\det( {{\overset{\_}{I}}_{M} + {\sigma_{d}^{2}{\overset{\_}{H}}_{0}^{H}{\overset{\overset{\_}{\_}}{R}}^{- 1}{\overset{\_}{H}}_{0}}} )}}}\end{matrix} & (11)\end{matrix}$where the last equality is a result of the identity log det(Ī+Ā B)=logdet(Ī+ BA). From equations (9) and (11), one can verify that this upperbound is achieved by setting W _(MC)= R ⁻¹ H ₀Ā for any invertiblematrix Ā, i.e., I( d _(i); r _(i)( W _(MC))| H)=I( d _(i);y_(i+F;i−F)|H).

The above Theorem does not imply that the filter W _(MC) is informationlossless. In fact, it can be shown that by transforming the channel Hfrom multi-path to single path, the filter W is always lossy. This isbecause the recovered mutual information is I( d _(i); y _(i+F;i−F))(where the condition on H is dropped for notational simplicity), whichis always smaller than the overall mutual information of the channel I(d _(i), d _(ī)y_(i+F;i−F)), where d _(ī) is viewed as signal and not asinterference. Therefore, the Theorem does imply that among the class oflossy filters that performs the multi-path to single-path channelconversion (which is necessary to avoid multi-user joint sequencedetection), the solution W _(MC) is the best one can hope for. Forpurposes of this disclosure, this reduced mutual information I( d _(i);y _(i+F;i−F)) is also referred to as the Constrained Mutual Information.

The concept of transforming a multi-path channel to a single-pathchannel is better known as chip-level equalization of CDMA downlink,mostly using LMMSE or MVDR algorithms. Defining an error vector of z= d_(i)− W ^(H)y_(i+F;i−F) and an error covariance matrix R _(zz)=E[ zz^(H)], the MIMO LMMSE chip-level equalizer W is the solution of thefollowing problem:

$\begin{matrix}\begin{matrix}{{\overset{\_}{W}}_{LMMSE} = {\arg\mspace{11mu}{\min\limits_{\;\overset{\_}{W}\;}\;{{Trace}( {\overset{\_}{R}}_{zz} )}}}} \\{= {\arg\mspace{11mu}{\min\limits_{\;\overset{\_}{W}\;}\;{E{{{\overset{\_}{d}}_{i} - {{\overset{\_}{W}}^{H}\;{\overset{\_}{y}}_{{i + F};{i - F}}}}}^{2}}}}}\end{matrix} & (12)\end{matrix}$whose optimal solution is given by W _(LMMSE)=σ_(d) ² R ⁻¹ H ₀.

Defining {circumflex over (d)}_(i,LMMSE)= W _(LMMSE) ^(H) y _(i+F;i−F))as the estimated chip vector, it is seen that this estimate is biased,since E└{circumflex over (d)}_(i,LMMSE)| d _(i)┘=σ_(d) ² H ₀ ² R ⁻¹ H ₀d _(i)≠ d _(i). An unbiased estimate can be obtained by solving insteadthe MIMO MVDR problem:

$\begin{matrix}{{{\overset{\_}{W}}_{MVDR} = {\arg\mspace{11mu}{\min\limits_{\;\overset{\_}{W}\;}\;{{Trace}( {{\overset{\_}{W}}^{H}\overset{\_}{R}\;\overset{\_}{W}} )}}}},{{{s.t.\mspace{14mu}\overset{\_}{W}}{\overset{\_}{H}}_{0}} = {\overset{\_}{I}}_{M}}} & (13)\end{matrix}$whose solution is W _(MVDR)= R ⁻¹ H ₀( H ₀ ^(H) R ⁻¹ H ₀)⁻¹. It followsthat the MVDR solution is a special case of the so-called FIR MIMOchannel-shortening filter described by N. Al-Dhahir in an articleentitled “FIR Channel-Shortening Equalizers for MIMO ISI Channels,” inIEEE Transactions on Communications, vol. 49, pp. 213-218, February2001.

The following corollary shows that both LMMSE and MVDR solutions areactually mutual information maximizing. This result shows that thesimple LMMSE or MVDR filter are the best achievable, as long as thesefilters are followed by joint detection in the spatial dimension.

Corollary: Both the LMMSE and MVDR equalizer solutions W _(LMMSE) and W_(MVDR), are mutual information maximizing.

Proof: The corollary is obvious for W _(MVDR) by setting and applyingthe above Theorem. On the other hand, with the help of matrix inversionlemma described by L. Scharf in Statistical Signal Processing.Detection, Estimation and Time Series Analysis (Addison Wesley, 1990),one can rewrite W _(LMMSE) asW _(LMMSE)=σ_(d) ² R ⁻¹ H ₀=σ_(d) ² R ⁻¹ H ₀(Ī _(M)+σ_(d) ² H ₀ ^(h) R⁻¹ H ₀)⁻¹  (14)and then set Ā=σ_(d) ²(Ī_(M)+σ_(d) ² H ₀ ^(H) R ⁻¹ H ₀)⁻¹ to completethe proof.

The CQI, or some other information derived from it such as a predictedframe error rate, may be transmitted by the receiver of FIG. 3A asfeedback to the transmitter, such as the transmitter 70 shown in blockdiagram at FIG. 3B. The transmitter 70 encodes at a first coding rate afirst input signal (or first set of information bits) at a joint encoder72 that encodes over at least two of space, time, and frequency. Themodulator 74 maps the encoded signal to a carrier waveform such as a16-QAM stored in a memory 76, which may be considered a firstmodulation. The memory stores at least two different modulations so thatthe transmitter may adapt its modulation scheme to the multi-pathchannel as described below. Preferably, the encoder 72 and modulator 74are combined into a signal space encoder that performs both functions,such as the coding and modulation block 34 of FIG. 1 where coding andmodulation are performed together rather than serially. The encoded andmodulated signal is then spread among the available spectrum andscrambled at the spreader block 78 using Walsh-type spreading codes 80previously detailed. The spread and scrambled signal is then dividedamong the M (M=2 shown) transmit antennas 84 by a router 82, andtransmitted over the multi-path channel. The router 82 may use awater-filling algorithm to divide packets among the transmit antennas 84for maximum capacity given channel quality. That channel quality may beprovided in the feedback 86 described immediately below.

In accordance with the invention, the transmitter 70 uses a feedback 86to adapt future transmissions to the channel as represented by a channelquality indicator CQI, specifically by changing modulation, coding rate,or both, based on the feedback 86 that is received from the recipient ofthe first signal sent over the multi-path channel. Two varieties of CQIare detailed below. The feedback 86 may come to the transmitter 70through the multi-path channel itself, a side channel, a dedicatedfeedback channel, and the like; the present invention is not limited toa particular feedback pathway. The feedback 86 need not be the CQIitself, but may be an estimate of frame error rate based on the CQI, aninstruction for the transmitter 70 to change coding rate and/ormodulation, or any intermediate figure of merit derived from the CQI. Ineither case, the feedback 86 is calculated in the receiver thattransmits it, such as that of FIG. 3A.

A processor 88 in the transmitter receives the feedback 86, and inresponse, causes the encoder 72 to change coding rate, the modulator 74to change a modulation, or both, for a second signal to be transmittedover the multi-path channel subsequent to the first. Coding rate andmodulation may be changed as in Table 2 below, and packet size may bechanged to comport with the adapted coding rate and modulation scheme asin Table 3 below, each adaptation based on the CQI that represents themulti-path channel. As the transmitter 70 and the receiver 48 eachtransmit and receive in an overall communication system, the describedtransmitter 70 may be considered a first transceiver and the describedreceiver 48 may be considered a second transceiver in a wirelessmulti-path communication system.

For MIMO transmission schemes that involve joint space-time encoding,the FER (SNR) curve is not well defined since each receive antenna 28sees a different SNR. Although in principle a multi-dimensional mappingFER(SNR₁, . . . , SNR_(M)) may always be defined, it is practicallyundesirable, if not impossible, due to the large amount of informationneeded for each link mapping. Proposed are two alternative MIMO linkmapping methods to overcome this difficulty. Apparently, the key tosolving the problem is to find a single channel quality indicator (CQI)that fully characterize the MIMO link. One way of doing that is to usethe so-called Generalized SNR (GSNR):

$\begin{matrix}{{GSNR}_{k}\hat{=}{\beta_{k}\frac{{Trace}( {\sigma_{d}^{2}{\overset{\_}{I}}_{M}} )}{{Trace}( {{\overset{\_}{R}}_{zz}( {\overset{\_}{W}}_{LMMSE} )} )}}} & (15)\end{matrix}$where R _(zz) is defined above at equation (12) and β_(k){circumflexover (=)}σ_(k) ²G is a scalar factor that translates the chip-level SNR(SNR of d _(i)) to the symbol-level SNR (SNR of t _(i)(j)). In mostpractical situations, the symbol amplitudes σ_(k) are the same for thoseWalsh codes that belong to the same user, i.e., σ₁= . . . =σ_(K) ₁ , andtherefore GNSR=GNSR₁= . . . =GSNR_(K) ₁ . Thus the link to systemmapping is reduced back to a single dimensional mapping FER(GSNR).

An alternative approach is to use the constrained mutual informationdetailed above as the single CQI that characterizes the MIMO link. It isimportant to recognize that the constrained mutual information I( d_(i); y _(i+F;i−F)) is obtained with the assumption that modulation andcoding are applied directly on the chip signals d _(i). Since in arealistic CDMA system the modulation and coding are always applied onsymbol signals α _(k)(j) it is better to use the symbol level mutualinformation I( α _(k)(j), t _(k)(j)) as the CQI of the link. However,once the front-end filter W _(MC)= R ⁻¹ H ₀Ā is fixed in FIG. 3, it isstraightforward to show thatI( α _(k)(j); t _(k)(j))=log det(Ī _(M)+β_(k)σ_(d) ² H ₀ ^(H) R ⁻¹ H₀)  (16)

Therefore, an alternative choice of single dimensional mapping is

${FER}( {\frac{1}{K_{1}}{\sum\limits_{k = 1}^{K_{1}}{I( {{{\overset{\_}{a}}_{k}(j)};{{\overset{\_}{t}}_{k}(j)}} )}}} )$where the CQI is the average mutual information across the K₁ Walshcodes assigned to the user. Note that here the condition σ₁= . . .=σ_(K) ₁ , is not necessary.

The difference between chip and symbol mutual information suggests thatthe filter block W and the following block (down-conversion, etc) inFIG. 3A might be combined into a composite filter block, and thendirectly optimize this composite filter. However, a closer examinationshows that doing that increases the notational complexity significantlywithout revealing much additional insight about the problem. Therefore,the inventors elect to stay with the loosely defined chip-level mutualinformation in this disclosure. The chip vs. symbol mutual informationis analogous to the chip vs. symbol level equalization problem known inthe art.

The algorithms and concepts described above have been evaluated in arealistic link-level simulator conforming to the CDMA2000 1x EV-DVstandard. The simulation results are presented in two parts. First isdemonstrated the usefulness of the constrained mutual information as theCQI measure to drive the link adaptation process for space-time jointlyencoded systems, by comparing the performance of coded VBLAST and PARCsystems in the presence of link adaptation. Second, it is shown theeffectiveness of the two CQI measures discussed above with reference toequations (15) and (16) in the context of link to system mapping,assuming that coded VBLAST scheme is used at the transmitter. Note thatalthough this disclosure has focused on the coded VBLAST and PARCschemes, the algorithms and concepts described here can be extended toother more complicated MIMO transmission schemes.

The simulation parameters used are tabulated in Table 1 below. The codedVBLAST scheme is used to demonstrate the usefulness of the constrainedmutual information I( d _(i); y _(i+F;i−F)) as the CQI in the presenceof link adaptation.

TABLE 1 Simulation parameters. Parameter Name Parameter Value SystemCDMA 1× EV-DV Spreading Length 32 Channel Profile Vehicular A MobileSpeed 30 km/h Filter Length 16 Number of Tx/Rx Antennas 2/2 ModulationFormat QPSK Total number of Active Walsh 25 Codes in the system

For comparison, performance of the PARC scheme is also shown where thesignals at the transmitter are separately encoded. The PARC schemeassumes a successive decoding structure that the prior art has shown tobe capacity achieving for a memoryless channel. These results areextended to a frequency-selective channel, where it is shown that in amulti-path channel, successive decoding achieves the constrained mutualinformation detailed above. Note that in the PARC scheme, a joint CQIlike I( d _(i); y _(i+F;i−F)) is not feasible since each antenna isseparately encoded.

In order to demonstrate the performance of MIMO schemes with linkadaptation, the parameters of each packet transmission are derived fromTable 2, consisting of 4 sets of parameters, each set being known as amodulation and coding scheme (MCS). Table 2 is a subset of the 5-leveltable used in the paper “Contribution RL-040366, Draft Document forMultiple-Input Multiple Output in UTRA”, 3GPP TSG-RAN. In order toachieve these spectral efficiencies approximately, the set of parametersshown in Table 3 are used in the context of the 1x EV-DV packet datachannel. Note that to arrive at these effective coding rates in Table 3,each PARC packet is transmitted over 5 ms (4 slots) whereas each codedVBLAST packet is transmitted over 2.5 ms (2 slots). The throughputcomparison between coded VBLAST and PARC is shown in FIG. 4.

TABLE 2 Modulation and coding schemes for link adaptation. ModulationCoding rate Spectral efficiency 1 QPSK ¼ 0.5 2 QPSK ½ 1.0 3 16-QAM ½ 2.04 16-QAM ¾ 3.0

TABLE 3 1× EV-DV PDCH parameters for link adaptation (4 Walsh CodesAssigned). Packet size Modulation Coding 1 408 QPSK 0.2656 2 792 QPSK0.5156 3 1560 16-QAM 0.5078 4 2328 16-QAM 0.7578

Note that most of the simulation parameters are the same as those inTable 1, except that here the traffic E_(c)/I_(or), is fixed and theGeometry is allowed to vary. Of course, the MCS is also a variable inthis case due to link-adaptation. Perfect feedback with no delay isassumed for the link adaptation, i.e., the transmitter changes the MCSinstantaneously at the end of every frame. The results show that codedVBLAST outperforms PARC slightly in these simulations. In order toachieve a particular set of two capacities, the PARC scheme uses twosmaller packet sizes, while the coded VBLAST scheme will use one singlelarger packet size. The gain seen in FIG. 4 might be due to the increasein the size of the interleaver in the turbo code due to a larger packetsize. On the other hand, PARC has more flexibility with respect to linkadaptation, which is not fully utilized in this simulation, where only asmall set of MCS schemes are used. More granularity in the linkadaptation might lead to different results.

For link-to-system mapping, computer simulations are used to obtain theFER(CQI) curves for the mapping the coded VBLAST scheme. Specifically,two channel metrics, GSNR and the constrained mutual information I( d_(i); y _(i+F;i−F)), which are detailed above, are exploited in FIGS. 5and 6. These two metrics enable characterization of the MIMO link by asingle CQI so that the multi-dimensional mapping methods can be avoided.

In the simulations, the spatial channel model (SCM) is assumed and theUrban Macro scenario is implemented, each as specified in 3GPP-3GPP2 SCMAHG, “3SCM-132:Spatial Channel Model Text Description,” April 2003. InSCM, the channel delay profile is a random vector, with a differentmultipath channel profile for each realization. Ten such independentrealizations of this random vector are used.

The LMMSE receiver followed by the per-Walsh joint detection algorithmas described above is employed. The parameters of the link areillustrated in Table I (except Geometry is set to zero in thesimulations presented in FIGS. 5-6). FIG. 5 plots the FER as a functionof the instantaneous value of the GSNR, while FIG. 6 provides a similarplot with respect to the constrained mutual information. For any givenCQI measure, the lesser the variation of the curves with differentrealizations, the more effective the measure is as an indicator of linkquality. Given this criterion, the constrained mutual information isseen to be more suitable compared to the GSNR.

In summary, this disclosure characterizes the use of constrained mutualinformation as the channel quality indicator (CQI) for space-timejointly encoded MIMO CDMA systems in frequency selective channels. Sucha CQI measure is shown to be essential for both link adaptation and alsoto provide a means of link-to-system mapping for jointly encoded MIMOCDMA system.

While there has been illustrated and described what is at presentconsidered to be preferred and alternative embodiments of the claimedinvention, it will be appreciated that numerous changes andmodifications are likely to occur to those skilled in the art. It isintended in the appended claims to cover all those changes andmodifications that fall within the spirit and scope of the claimedinvention.

1. A method comprising: receiving a jointly encoded signal over amulti-path channel by N receive antennas, wherein N is an integergreater than one; estimating a transmitted chip vector within a chipinterval for the jointly encoded signal; for each of the N receiveantennas, sampling the received signal to resolve an antenna-wisereceived chip vector for each of the N receive antennas; filtering ablock of antenna-wise received chip vectors, the block comprising eachof the N antenna-wise chip vectors, using a channel quality indicatorthat maximizes constrained mutual information between the estimatedtransmitted chip vector and the block of antenna-wise received chipvectors; downconverting the filtered block to one of bits and symbols;detecting in parallel, for each spreading code by which the jointlyencoded signal is spread, the downconverted bits or symbols, wherein thechannel quality indicator describes the entire multi-path channel overwhich the signal was received by the N antennas and wherein the channelquality indicator comprises a generalized signal to noise ratio thatrepresents all channel uses of the multiple input/multiple outputmulti-path channel over which the jointly encoded signal was received.2. The method of claim 1 wherein the generalized signal to noise ratiois computed by:${{GSNR}_{k}\hat{=}{\beta_{k}\frac{{Trace}( {\sigma_{d}^{2}{\overset{\_}{I}}_{M}} )}{{Trace}( {{\overset{\_}{R}}_{zz}( {\overset{\_}{W}}_{LMMSE} )} )}}};$where β_(k) is a scalar factor that translates a chip-level signal tonoise ratio to a symbol-level signal to noise ratio for the user; σ_(d)² is noise variance with respect to the estimated transmitted chipvector; Ī_(M) is an identity matrix of size M×M, wherein M is a numberof transmit antennas from which the encoded signal was sent; R _(zz) isan error covariance matrix; and W _(LMMSE) is the filtered block ofantenna-wise chip vectors.
 3. The method of claim 1 wherein filteringcomprises filtering such that W _(MC)= R ⁻¹ H ₀Ā; where R{circumflexover (=)}σ_(d) ² H ₀ H ₀ ^(H)+σ²Ī; σ_(d) ² is noise variance withrespect to the estimated transmitted chip vector; σ² is noise withrespect the block of antenna-wise received chip vectors; Ī is anidentity matrix H ₀ is a memoryless multi-path channel estimate matrix,and superscript^(H) indicates a Hermitian operation; and Ā is anarbitrary invertible matrix.
 4. The method of claim 1 wherein theconstrained mutual information I( d _(i) y _(i−Fi+F)| H) is maximizedsuch that I( d _(i) y _(i−F:i+F)| H)=log det(Ī_(M)+σ_(d) ² H ₀ ^(H) R ⁻¹H ₀); wherein Ī_(M) is an identity matrix of size M×M, wherein M is anumber of transmit antennas from which the encoded signal was sent;σ_(d) ² is noise variance with respect to the estimated transmitted chipvector; H ₀ is a memoryless multi-path channel estimate matrix, andsuperscript ^(H) indicates a Hermitian operation; R{circumflex over(=)}σ_(d) ² H ₀ H ₀ ^(H)+σ²Ī; σ² is noise vaiance with respect to theblock of antenna-wise received chip vectors; and Ī is an identitymatrix.
 5. The method of claim 1, wherein filtering comprises employinga bank of linear minimum mean square error filters W _(LMMSE) thatfilter the block of antenna-wise chip vectors according to W_(LMMSE)=σ_(d) ² R ⁻¹ H ₀; where: σ_(d) ² is noise variance with respectto the estimated transmitted chip vector; H ₀ is a memoryless multi-pathchannel estimate matrix; and R ⁻¹ is an inverted covariance matrix ofthe received block of antenna-wise received chip vectors.
 6. The methodof claim 1, wherein filtering comprises employing a bank of minimumvariance distortionless response linear filters W _(MVDR) that filterthe block of antenna-wise chip vectors according to W _(MVDR)= R ⁻¹ H ₀(H ₀ ^(H) R ⁻¹ H ₀)⁻¹; where: H ₀ is a memoryless multi-path channelestimate matrix, and superscript ^(H) indicates a Hermitian operation;R{circumflex over (=)}σ_(d) ² H ₀ H ₀ ^(H)+σ²Ī; σ_(d) ² is noisevariance with respect to the estimated transmitted chip vector; σ² isnoise vairance with respect to the block of antenna-wise received chipvectors and Ī is an identity matrix.
 7. A method comprising: receiving ajointly encoded, spread spectrum signal from a multi-path channel overat least two receive antennas in a chip interval; estimating atransmitted chip vector for the signal; sampling the signal to achieve achip-wise signal vector from each receive antenna; storing the chip-wisesignal vectors as a block; estimating the multi-path channel bydetermining mutual information between the block of chip-wise signalvectors and the estimated transmitted chip vector, wherein the estimatedmulti-path channel describes the entire multi-path channel over whichthe signal was received by the N antennas and wherein the estimatedmulti-path channel comprises a generalized signal to noise ratio thatrepresents all channel uses of the multiple input/multiple outputmulti-path channel over which the jointly encoded signal was received;using the estimate of the multi-path channel, filtering the block ofchip-wise signal vectors to restore orthogonality to spreading codesused to spread the signal; down-converting, descrambling and despreadingthe filtered block of chip-wise signal vectors to yield parallel outputsof symbol-level signal vectors, each parallel output corresponding to aspreading code; and for each of the parallel outputs, spatiallydetecting one of bits or symbols using one spreading code.
 8. The methodof claim 7 wherein filtering the block of chip-wise signal vectorscomprises passing the block of chip-wise signal vectors through a linearminimum mean square error filter.
 9. The method of claim 8, wherein thelinear minimum mean square error filter operates on the block ofchip-wise signal vectors according to W _(LMMSE)=σ_(d) ² R ⁻¹ H ₀;where: σ_(d) ² is noise variance with respect to the estimatedtransmitted chip vector; H ₀ is a memoryless multi-path channel estimatematrix; and R ⁻¹ is an inverted covariance matrix of the block ofantenna-wise received chip vectors.
 10. The method of claim 7 whereinfiltering the block of chip-wise signal vectors comprises passing theblock of chip-wise signal vectors through a minimum variancedistortionless response filter.
 11. The method of claim 10 wherein theminimum variance distortionless response filter operates on the block ofchip-wise signal vectors according to W _(MVDR)= R ⁻¹ H ₀( H ₀ ^(H) R ⁻¹H ₀)⁻¹; where: H ₀ is a memoryless multi-path channel estimate matrix,and superscript ^(H) indicates a Hermitian operation; R{circumflex over(=)}σ_(d) ² H ₀ H ₀ ^(H)+σ²Ī; σ_(d) ² is noise variance with respect tothe estimated transmitted chip vector; σ² is noise variance with respectto the block of chip-wise signal vectors and Ī is an identity matrix.12. The method of claim 7 wherein spatially detecting one of bits orsymbols comprises spatially detecting bits for the case where thereceived signal is jointly encoded with a binary code.
 13. The method ofclaim 7 wherein filtering the block of chip-wise signal vectors torestore orthogonality comprises equalizing sub-channels of themulti-path channel.
 14. The method of claim 7 wherein determining mutualinformation comprises determining a maximized constrained mutualinformation I( d _(i) y _(i−F:i+F)| H), such that I( d _(i) y_(i−F:i+F)| H)=log det(Ī_(M)+σ_(d) ² H ₀ ^(H) R ⁻¹ H ₀); wherein Ī_(M)is an identity matrix of size M×M, wherein M is a number of transmitantennas from which the encoded signal was sent; σ_(d) ² is noisevariance with respect to the estimated transmitted chip vector; H ₀ is amemoryless multi-path channel estimate matrix, and superscript ^(H)indicates a Hermitian operation; R{circumflex over (=)}σ_(d) ² H ₀ H ₀^(H)+σ²Ī; σ² is noise variance with respect to the block of chip-wisesignal vectors; and Ī is an identity matrix.
 15. A method comprising: ina first transceiver, jointly encoding a first signal to be transmittedat a first coding rate, modulating the first signal to be transmittedwith a first modulation, and transmitting the jointly encoded andmodulated first signal over a spread spectrum multi-path wirelesschannel by at least one transmit antenna; in a second transceiver,receiving the jointly encoded and modulated first signal by at least tworeceive antennas over the multi-path channel; converting the multi-pathchannel over which the first signal was received to an effectivesingle-path channel; determining a single channel quality indicator thatcharacterizes the effective single-path channel by constraining mutualinformation between an estimate of a transmitted chip and a block ofantenna-wise received chip vectors; from the effective single-pathchannel, detecting in parallel one of bits and symbols, each paralleldetection being according to one spreading code by which the firstsignal is spread over the spectrum; and transmitting to the firsttransceiver a feedback based on the channel quality indicator, whereinthe channel quality indicator describes the entire multi-path channelover which the signal was received by the N antennas and wherein thechannel quality indicator comprises a generalized signal to noise ratiothat represents all channel uses of the multiple input/multiple outputmulti-path channel over which the jointly encoded signal was received;and in the first transceiver, receiving the feedback; jointly encoding asecond signal to be transmitted, modulating the second signal to betransmitted, and transmitting the jointly encoded and modulated secondsignal over a spread spectrum multi-path wireless channel by the atleast one transmit antenna, wherein, in response to the feedback, thesecond signal is at least one of encoded at a second coding rate andmodulated with a second modulation.
 16. The method of claim 15 whereinthe feedback is an estimated error rate that is calculated from thechannel quality indicator.
 17. The method of claim 15 wherein thefeedback is an instruction to change at least one of coding rate andmodulation format based on the channel quality indicator.
 18. The methodof claim 15 wherein the channel quality indicator is a generalizedsignal to noise ratio for the second transceiver such that${{GSNR}_{k}\hat{=}{\beta_{k}\frac{{Trace}( {\sigma_{d}^{2}{\overset{\_}{I}}_{M}} )}{{Trace}( {{\overset{\_}{R}}_{zz}( {\overset{\_}{W}}_{LMMSE} )} )}}};$where β_(k) is a scalar factor that translates a chip-level signal tonoise ratio to a symbol-level signal to noise ratio for the user; σ_(d)² is noise variance with respect to the estimated transmitted chipvector; Ī_(M) is an identity matrix of size M×M, wherein M is a numberof transmit antennas in the first transceiver from which the encodedsignal was sent; R _(zz) is an error covariance matrix; and W _(LMMSE)is the filtered block of antenna-wise received chip vectors.
 19. Themethod of claim 16 wherein the feedback is the generalized signal tonoise ratio.
 20. The method of claim 17 wherein the channel qualityindicator is maximized constrained mutual information I( d _(i) y_(2F+1)| H)=log det(Ī_(M)+σ_(d) ² H ₀ ^(H) R ⁻¹ H ₀).
 21. The method ofclaim 20 wherein the feedback is the maximized constrained mutualinformation.
 22. A receiver comprising: at least two receive antennas; afilter bank of linear filters having a first input coupled to an outputof each receive antenna and a second input, said filter bank forequalizing signal vectors received over sub-channels of a multipathchannel into signal vectors of a single channel by constraining mutualinformation between an estimate of a transmitted chip and a block ofantenna-wise received chip vectors; a channel estimator having an inputcoupled to an output of each receive antenna and an output coupled tothe second input of the filter bank, wherein channel estimator isconfigured to estimate the multi-path channel which describes the entiremulti-path channel over which the signal was received by the at leasttwo receive antennas and wherein the estimated multi-path channelcomprises a generalized signal to noise ratio that represents allchannel uses of the multi-path channel over which the jointly encodedsignal was received; a plurality of joint detectors in parallel with oneanother, each joint detector having an input coupled to an output of thefilter bank and an output coupled to a decoder, each joint detector fordetecting one of bits or symbols according to one spreading code; achip-to-symbol down-converter, a de-scrambler, and a de-spreader, eachdisposed between the filter bank and the plurality of joint detectors.23. The receiver of claim 22 wherein the filter bank comprises a bank oflinear minimum mean square error filters that operate according to W_(LMMSE)=σ_(d) ² R ⁻¹ H ₀; where: σ_(d) ² is noise variance with respectto the estimated transmitted chip vector; H ₀ is a memoryless multi-pathchannel estimate matrix; and R ⁻¹ is an inverted covariance matrix ofthe antenna-wise received chip vectors.
 24. The receiver of claim 22wherein the filter bank comprises a bank of minimum variancedistortionless response filters that operate according to W _(MVDR)= R⁻¹ H ₀( H ₀ ^(H) R ⁻¹ H ₀)⁻¹; where: H ₀ is a memoryless multi-pathchannel estimate matrix, and superscript ^(H) indicates a Hermitianoperation; R{circumflex over (=)}σ_(d) ² H ₀ H ₀ ^(H)+σ²Ī; σ_(d) ² isnoise variance with respect to the estimated transmitted chip vector; σ²is noise variance with respect to the block of chip-wise signal vectorsand Ī is an identity matrix.
 25. The receiver of claim 22 wherein eachof the plurality of joint detectors is a spatial detector.
 26. Thereceiver of claim 22 having N receive antennas, wherein the filter bankoperates to maximize constrained mutual information I( d _(i) y_(i−F:i+F)| H), such that I( d _(i) y _(i−F:i+F)| H)=log det(Ī_(M)+σ_(d)² H ₀ ^(H) R ⁻¹ H ₀); wherein σ_(d) ² is noise variance with respect tothe estimated transmitted chip vector; H ₀ is a memoryless multi-pathchannel estimate matrix, and superscript ^(H) indicates a Hermitianoperation; R{circumflex over (=)}σ_(d) ² H ₀ H ₀ ^(H)+σ²Ī; σ² is noisevariance with respect to the block of chip-wise signal vectors; and Ī isan identity matrix.
 27. An apparatus comprising: a processing unitconfigured to compute a channel quality indicator of a channelcomprising multiple streams; and a transmitter configured to transmitthe channel quality indicator, where the channel quality indicatoraverages constrained mutual information between an estimated transmittedchip vector within a chip interval for a jointly encoded signal receivedover a multi-path channel by N receive antennas, wherein N is an integergreater than one and a filtered block of antenna-wise received chipvectors comprising each of the N antenna-wise chip vectors, wherein thechannel quality indicator describes the entire multi-path channel overwhich the signal was received by the N antennas and wherein the channelquality indicator comprises a generalized signal to noise ratio thatrepresents all channel uses of the multiple input/multiple outputmulti-path channel over which the jointly encoded signal was received.28. The apparatus as in claim 27, where the channel quality indicator isfurther computed according to log det(Ī_(M)+σ_(d) ² H ₀ ^(H) R ⁻¹ H ₀),wherein Ī_(M) is an identity matrix of size M×M, wherein M is a numberof transmit antennas from which the encoded signal was sent; σ_(d) ² isa scalar factor; H ₀ is a memoryless multi-path channel estimate matrix,and superscript H indicates a Hermitian operation; R{circumflex over(=)}σ_(d) ² H ₀ H ₀ ^(H)+σ²Ī; σ² is noise variance with respect to theblock of chip-wise signal vectors; and Ī is an identity matrix.
 29. Theapparatus of claim 27, where the channel quality indicator is furthercomputed according to${\beta_{k}\frac{{Trace}( {\sigma_{d}^{2}{\overset{\_}{I}}_{M}} )}{{Trace}( {{\overset{\_}{R}}_{zz}( {\overset{\_}{W}}_{LMMSE} )} )}};$where β_(k) is a scalar factor that translates a chip-level signal tonoise ratio to a symbol-level signal to noise ratio for the user; σ_(d)² is a scalar factor; Ī_(M) is an identity matrix of size M×M, wherein Mis a number of transmit antennas in the first transceiver from which theencoded signal was sent; R _(zz) is an error covariance matrix; and W_(LMMSE) is the filtered block of antenna-wise received chip vectors.30. The apparatus as in claim 27, where the channel is a transmissionchannel originating from a transmitter, the channel quality indicatorbeing fed back to the transmitter.
 31. An apparatus comprising: areceiver configured to receive a channel quality indicator of a channelcomprising multiple streams; and a processing unit configured to adjust,on the bases of the channel quality indicator, a transmission of themultiple streams on the channel, where the channel quality indicatoraverages constrained mutual information between an estimated transmittedchip vector within a chip interval for a jointly encoded signal receivedover a multi-path channel by N receive antennas, wherein N is an integergreater than one and a filtered block of antenna-wise received chipvectors comprising each of the N antenna-wise chip vectors, wherein thechannel quality indicator describes the entire multi-path channel overwhich the signal was received by the N antennas and wherein the channelquality indicator comprises a generalized signal to noise ratio thatrepresents all channel uses of the multiple input/multiple outputmulti-path channel over which the jointly encoded signal was received.32. The apparatus of claim 31, where the channel quality indicatorrepresents the channel according to log det(Ī_(M)+σ_(d) ² H ₀ ^(H) R ⁻¹H ₀), wherein Ī_(M) is an identity matrix of size M×M, wherein M is anumber of transmit antennas from which the encoded signal was sent;σ_(d) ² is a scalar factor; H ₀ is a memoryless multi-path channelestimate matrix, and superscript H indicates a Hermitian operation;R{circumflex over (=)}σ_(d) ² H ₀ H ₀ ^(H)+σ²Ī; σ² is noise variancewith respect to the block of chip-wise signal vectors; and Ī is anidentity matrix.
 33. The apparatus of claim 31, where the channelquality indicator represents the channel according to${\beta_{k}\frac{{Trace}( {\sigma_{d}^{2}{\overset{\_}{I}}_{M}} )}{{Trace}( {{\overset{\_}{R}}_{zz}( {\overset{\_}{W}}_{LMMSE} )} )}};$where β_(k) is a scalar factor that translates a chip-level signal tonoise ratio to a symbol-level signal to noise ratio for the user; σ_(d)² is a scalar factor; Ī_(M) is an identity matrix of size M×M, wherein Mis a number of transmit antennas in the first transceiver from which theencoded signal was sent; R _(zz) is an error covariance matrix; and W_(LMMSE) is the filtered block of antenna-wise received chip vectors.34. The apparatus of claim 31, further comprising a transmitter fortransmitting the multiple streams.